Reflet de la pandémie de Covid-19 dans les dictionnaires de la langue française
Bibliographic record
Abstract
The Reflection of the COVID-19 Pandemic in Dictionaries of the French Language. Languages adapt to reflect changes taking place in the life of users. The COVID-19 pandemic, by its specificity, has had an enriching effect on the French language, which quickly created and borrowed simple and complex lexical units, a new specialized vocabulary reflecting the transformations that have occurred in the society. Medical terms like coronavirus (type of virus) and COVID-19 (disease caused by SARS -CoV-2) became a part of everyday conversation. New words and new meanings are usually added to dictionaries once editors have enough evidence to demonstrate continued historical use; therefore, they must be used over a significant period of time to earn their place in dictionaries. Based on the above, the question arises: how has the epidemic impacted dictionary editors? The present study attempts to investigate the new French words and expressions that emerged in the wake of the COVID-19 crisis and were added to dictionaries of the French language. The corpus collected within this study comprises neologisms (the concept of neologism can be misleading here, because the lexicography theory characterizes neologisms as words which have not been included in current dictionaries) from various fields, new words or expressions and new meanings which have been added to the Petit Robert and the Petit Larousse illustré and the online dictionaries Grand dictionnaire terminologique and Wiktionnaire. The French dictionary Le Petit Robert has added 26 new words and meanings to its 2022 dictionary, and 48 new words and meanings, from cluster to coronapiste (a cycle lane introduced during the COVID-19 crisis), have entered the French dictionary Le Petit Larousse 2022. The present study shows how French dictionaries have been able to adapt to the changes brought about by the COVID-19 pandemic. During this health crisis, the French language has been extraordinarily dynamic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".